Papers Facial Expression Recognition
“Facial Expression Recognition” 태그가 달린 논문 596편 · 필터 해제
Predicting Human Disagreement for Calibrated Dynamic Facial Expression Recognition
Dynamic facial expression recognition (DFER) benchmarks such as DFEW provide multiple annotator votes per clip, yet most models collapse them to a majority label and cannot represent human disagreement at inference time.…
Facial Expression RecognitionAUCH-Net: Action Unit-Based Consistency-Aware Hypergraph Network for Cross-Domain Few-Shot Facial Expression Recognition
Recently, cross-domain few-shot facial expression recognition (CF-FER) has received considerable attention. However, the performance of existing CF-FER methods is still unsatisfactory due to inferior transferable feature…
Facial Expression RecognitionCross-Domain Few-ShotEnhancing Facial Expression Recognition in Head-Mounted Displays with Synthetic Data
Facial expression recognition (FER) is crucial for social interaction in mixed reality environments that employ head-mounted displays (HMD). However, collecting FER data from head-mounted cameras (HMC) is challenging due…
Facial Expression RecognitionReweighting Framewise Attention in Video Transformers for Facial Expression Understanding
Understanding facial expressions in videos requires modeling subtle and localized facial dynamics under unconstrained conditions. Although recent Vision Transformer (ViT)-based video models have shown strong performance …
Facial Expression RecognitionInterpretable Uncertainty Routing Separating Emotion Ambiguity from Distribution Shift in Facial Expression Recognition
Facial expression recognition (FER) is inherently ambiguous: human annotators frequently disagree, and models deployed in real environments face distribution shift. Crucially, these two conditions demand different downst…
Facial Expression RecognitionChehre: An Emoji-Prompted Video Dataset for Perceptually Diverse Facial Expression Recognition
Facial expressions are nonverbal social signals used in human interaction, but facial expression recognition datasets often focus on static images, basic emotion categories, or single deterministic annotations. We introd…
Facial Expression RecognitionFacial Expression Recognition in the Deep Learning Era: A Systematic Multi-Criteria Review of Methods, Models, Datasets, Performance, Challenges, and Future Research Directions
Facial Expression Recognition (FER) has advanced rapidly over the last decade, driven by the shift from handcrafted descriptors and shallow classifiers to deep convolutional, attention-based, vision-language, and foundat…
Facial Expression RecognitionBeyond Universality: The GCC-FER Dataset and Culture-Aware Adaptation for Dynamic Facial Expression Recognition
Dynamic Facial Expression Recognition (DFER) is a key enabling technology in affective computing, human-computer interaction, and intelligent multimedia systems. Despite the significant influence of cultural nuances on F…
Facial Expression RecognitionLaCoVL-FER: Landmark-Guided Contrastive Learning Network with Vision-Language Enhancement for Facial Expression Recognition
Facial Expression Recognition (FER) in the wild requires models to identify subtle expression cues under large variations in pose, occlusion, illumination, and identity. Recent FER methods improve robustness by introduci…
Facial Expression RecognitionContrastive LearningOn Applicability of Synthetic Datasets for Facial Expression Recognition
Facial Expression Recognition faces two core challenges. The first is class imbalance in public datasets, which skews the learning process and weakens generalization. The second is related to privacy and data collection …
Facial Expression RecognitionA cross-modal network for facial expression recognition
Deep neural networks enriched with structural information have been widely employed for facial expression recognition tasks. However, these methods often depend on hierarchical information rather than face property to fi…
Facial Expression RecognitionFace AlignmentWhy Do Vision Language Models Struggle To Recognize Human Emotions?
Understanding emotions is a fundamental ability for intelligent systems to be able to interact with humans. Vision-language models (VLMs) have made tremendous progress in the last few years for many visual tasks, potenti…
Facial Expression RecognitionEmotion RecognitionARGen: Affect-Reinforced Generative Augmentation towards Vision-based Dynamic Emotion Perception
Dynamic facial expression recognition in the wild remains challenging due to data scarcity and long-tail distributions, which hinder models from effectively learning the temporal dynamics of scarce emotions. To address t…
Facial Expression RecognitionReinforcement LearningCognition-Inspired Dual-Stream Semantic Enhancement for Vision-Based Dynamic Emotion Modeling
The human brain constructs emotional percepts not by processing facial expressions in isolation, but through a dynamic, hierarchical integration of sensory input with semantic and contextual knowledge. However, existing …
Facial Expression RecognitionProduct Review Based on Optimized Facial Expression Detection
This paper proposes a method to review public acceptance of products based on their brand by analyzing the facial expression of the customer intending to buy the product from a supermarket or hypermarket. In such cases, …
Facial Expression RecognitionActFER: Agentic Facial Expression Recognition via Active Tool-Augmented Visual Reasoning
Recent advances in Multimodal Large Language Models (MLLMs) have created new opportunities for facial expression recognition (FER), moving it beyond pure label prediction toward reasoning-based affect understanding. Howe…
Facial Expression RecognitionReinforcement LearningMultimodal ReasoningVisual ReasoningEmotion Diffusion Classifier with Adaptive Margin Discrepancy Training for Facial Expression Recognition
Facial Expression Recognition (FER) is essential for human-machine interaction, as it enables machines to interpret human emotions and internal states from facial affective behaviors. Although deep learning has significa…
Facial Expression RecognitionAdversarial RobustnessCLIP-AUTT: Test-Time Personalization with Action Unit Prompting for Fine-Grained Video Emotion Recognition
Personalization in emotion recognition (ER) is essential for accurate interpretation of subtle and subject-specific expressive patterns. Recent advances in vision-language models (VLMs), such as CLIP, demonstrate strong …
Facial Expression RecognitionVideo Emotion RecognitionTest-Time Adaptation via Cache Personalization for Facial Expression Recognition in Videos
Facial expression recognition (FER) in videos requires model personalization to capture considerable variations across subjects. Vision-language models (VLMs) offer strong transfer to downstream tasks through image-text …
Facial Expression RecognitionTest-time AdaptationKnowledge Priors for Identity-Disentangled Open-Set Privacy-Preserving Video FER
Facial expression recognition relies on facial data that inherently expose identity and thus raise significant privacy concerns. Current privacy-preserving methods typically fail in realistic open-set video settings wher…
Facial Expression Recognition